Desirability Function Approach Research Articles

Optimization of Microwave-Assisted Polyphenol Extraction and Antioxidant Activity from Papaya Peel Using Response Surface Methodology and Artificial Neural Network

The objective of the present study is to determine the optimal conditions of microwave-assisted extraction (MAE) such as microwave power (MWP), irradiation time (I-time), ethanol concentration (EtOH%), and solvent-to-solid ratio (S/S) for maximizing total polyphenol content (TPC) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity from papaya peel. Box-Behnken design (BBD) was used considering MWP 300–600 W, I-time 60-120 s, EtOH% 30-70%, and S/S 30-70 mL/g. Additionally, response surface methodology (RSM) and artificial neural networks (ANN) were employed for modelling, with cross-validation to enhance reliability. These models were combined with the desirability function (DF) and/or genetic algorithm (GA) optimization approaches. Maximizing TPC and DPPH activity while maintaining MWP, I-time, EtOH%, and S/S within their respective ranges using hybrid optimization approaches (RSM-DF: TPC = 1058 mgGAE/100g, and DPPH = 83%, RSM-GA: TPC = 1064 mgGAE/100g and DPPH = 79%, and ANN-GA: TPC = 1086 mgGAE/100g, and DPPH = 83%,) yielded consistent optimal results. An additional optimization approach (RSM-DF: TPC = 955 mgGAE/100g, and DPPH = 90%), aimed at minimizing MWP, I-time, EtOH%, and S/S while maximizing TPC and DPPH activity, showed a significant reduction of approximately 3 L in ethanol consumption for each 100 g of dry samples. The maximized TPC and DPPH activity in all four approaches showed a significant (p < 0.05) alignment with their corresponding validation tests, demonstrating the better performance of these modelling and optimization processes. In terms of assessing the effectiveness of microwave assistance, conventional solvent extraction was also performed, maintaining a 70 mL/g S/S ratio of 55% ethanol at 60°C for 2 h. This method resulted in lower values of TPC (857 ± 23 g GAE/100g) yield and DPPH activity (72 ± 5%) compared to the optimized condition of MAE.

Multiobjective optimization of magnetic abrasive finishing process with periodic re-distribution of magnetic abrasive particles on 17-4PH stainless steel

Magnetic abrasive finishing (MAF) exhibits considerable potential as a promising process for enhancing surface finish quality for softer as well as harder materials. This work proposes a novel approach to harness the potential of MAF for obtaining superior surface quality of precipitation-hardening martensitic steel (17-4PH steel) while performing periodic re-distribution of magnetic abrasive particles. The experiments were conducted using the Box-Behnken design of experiments and response surface methodology (RSM). RSM-based desirability function approach and genetic algorithms were used for multiobjective optimization. The set of pareto-optimal solutions obtained through multiobjective optimization were ranked using the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) by assigning equal importance to the selected performance parameters (percentage change in surface roughness (PCSR) and material removal rate (MRR)). The optimized values of PCSR and MRR obtained through the desirability function of RSM and genetic algorithm were validated experimentally and compared with the predicted values obtained through the regression model. Results showed that the desirability function of RSM provided better results than the genetic algorithm for the selected conditions and confirmed the effectiveness of the proposed approach.

Influence of drying method on the shelf life of artisanal pasta

Efforts to optimize pasta drying often face challenges due to the complexity of operational variables. This study aimed to optimize conventional fixed-bed convective drying of artisanal pasta using a desirability function approach and assess the influence of drying methods on shelf life. Three methods were compared: optimized conventional fixed-bed convective drying (O-CFBCD), hot air microwave drying (HAMD), and convective tray drying (CTD). This study underscores the importance of exploring and regulating pasta quality parameters to meet both industry standards and consumer preferences for marketable products with sensory appeal. While all three drying methods show promise for producing quality pasta, there is a need for improved initial moisture control, particularly in the CTD condition, to ensure compliance with regulatory standards and mitigate microbiological risks associated with high water content. Over time, most sensory parameters exhibited enhancement, aligning with consumer expectations for high-quality products. This suggests that continuous monitoring and optimization of drying processes are essential for maintaining and improving pasta quality throughout its shelf life.

Numerical investigation of the geometric parameters effect of helical blades installed on horizontal geo heat exchanger

In this study, we investigate the enhancement of horizontal geothermal heat exchangers equipped with helical fins on the pipe's exterior and internally ribbed turbulators. Our approach focuses on the interplay between geometry and thermal efficiency through innovative design modifications. Utilizing the finite element method, three-dimensional numerical simulations assessed the effects of varying geometric parameters such as the diameter and thickness of the fins. Our findings indicate significant increases in heat transfer efficiency with the addition of helical fins; specifically, increasing the fin diameter from 5 mm to 10 mm results in a 15 % increase in the heat transfer rate, while doubling the fin thickness from 2 mm to 4 mm enhances the rate by 10 %. These improvements are due to the expanded surface area facilitating greater heat exchange. Optimization using the desirability function approach yielded models with high performance, achieving desirability scores of 0.9879 for outlet temperature and 0.9534 for the heat transfer coefficient. This reflects the effective tuning of geometric parameters to maximize thermal performance. The study also introduces two predictive mathematical models for the outlet temperature and convective heat transfer coefficient of the U-shaped pipe equipped with these enhancements. These models, derived from extensive numerical data, provide practical tools for future design and operational applications of geothermal heat exchangers. This research advances the design and operational efficiency of geothermal heat exchange systems, establishing new benchmarks for thermal efficiency in the field with actionable insights and robust mathematical tools.

Immersion cooling of a cylindrical battery module: An optimum configuration using CFD, NSGA-II and desirability function approach

Immersion cooling offers promising advantages for cylindrical battery modules, particularly in applications where compact design, efficient thermal management, and enhanced safety are critical factors. In this study, the geometric, thermal, and dynamic parameters of an immersion-cooled battery module with 14 NCM cylindrical cells are analyzed using CFD methods. The developed battery model is experimentally validated, establishing the constraints through numerical analysis of the impact of design parameters on response values. The design parameters are optimized using a multi-objective optimization technique. The study models the battery module with mineral oil cooling and examines the impact of each parameter on variables such as maximum battery module temperature (Tmax), pressure drop (ΔP), maximum temperature variance (ΔTmax), and battery module mass index (Im). A Design of Experiment set, created with Central Composite Design (CCD), is solved using CFD methods, and quadratic correlation equations are derived using backward regression. The regression models for Tmax, ΔTmax, ΔP, and Im show high accuracy with R2 values close to 1.0. Optimization is performed using the entropy weight-based composite desirability function approach, yielding optimum values for cell center spacing (t), coolant mass flow rate (ṁc), and coolant inlet temperature (Ti) at 20.606 mm, 1.5 kg/s, and 23 °C, respectively. The optimized design sets are validated through CFD analysis, showing a maximum deviation of 4.27 %. Reducing t boosts thermal performance but lowers hydraulic performance, while the reverse occurs for ṁc. As Ti decreases, effective cooling improves, but temperature uniformity suffers.

Tribological behavior of a Ti42Nb42Mo6Fe5Cr5 complex concentrated alloy and prediction through response surface methodology based mathematical modeling

In the present work, a novel Ti42Nb42Mo6Fe5Cr5 complex concentrated alloy (CCA) was developed using the vacuum arc melting technique. The as-cast CCA underwent a two-stage heat treatment process. In the first stage of heat treatment (HT 1), the alloy was heated to 900˚C. Subsequently, in the second stage of heat treatment (HT 2), the HT 1 samples were annealed at various temperatures, including 700˚C, 900˚C, and 1100˚C, for 20 h. The microstructure and mechanical response of the as-cast, HT 1 and HT 2 (annealed) samples were thoroughly investigated. The phase evolution, microstructure, and chemical composition were analyzed using XRD and SEM-EDS. The XRD analysis revealed major solid solution BCC phases (BCC 1 and BCC 2) with a small amount of Laves phase; however, the amount of Laves phase increased with increase in annealing temperature. The microhardness and elastic modulus of the CCA specimens were evaluated using instrumented micro-indentation. The CCA annealed at 1100˚C exhibits the highest microhardness and elastic modulus, with values of 5.94 ± 0.38 GPa and 124.40 ± 7.17 GPa, respectively. Response surface methodology (RSM) was used to develop a mathematical model aimed at predicting tribological characteristics, specifically the specific wear rate (SWR) and coefficient of friction (COF). RSM proposes a quadratic model to represent the mathematical relationship between input parameters for evaluating SWR and COF. The desirability function approach is employed to optimize input parameters to minimize both SWR and COF. The optimized values of SWR and COF are 6.87 × 10−4 mm3/N.m and 0.30 under a 27.52 N load, 2.86 Hz oscillation frequency, and 1100˚C annealing temperature. Surface topography analysis of the worn surface was evaluated using SEM and a profilometer to understand the wear mechanism and surface characteristics.

Quality evaluation of selected expired fluoroquinolones medicines obtained from the public hospitals in Jimma zone, Oromia regional state, Ethiopia.

The problem of medicine expiration presents a notable obstacle, resulting in considerable financial losses. Nevertheless, there is currently limited data indicating that certain medications do not experience a significant decrease in effectiveness after their expiration date. Therefore, the aim of the study was to assess the physico-chemical quality of expired fluoroquinolone antibiotics. The expired samples of fluoroquinolone antibiotics were purposively collected from public hospitals in the Jimma zone of the Oromia regional state, Ethiopia. A World Health Organization quality evaluation sampling strategy was employed. Then, simple random sampling techniques were utilized for the selection of tablets for the laboratory quality control test. The assay, identification, and dissolution were performed in accordance with the United States Pharmacopeia (USP) guidelines, as well as failure mode and effect analysis (FMEA) techniques. The finding revealed that about 100% (7/7) expired samples passed pharmacopeia quality specifications for identity and assay tests. However, of the seven expired brands, about 14.3% (1/7) of the sample (Code-002) was unable to release its API content within the USP criteria of 30 min. The risk-based quality evaluation revealed that assay was the most critical quality attributed to ciprofloxacin tablets (RPN = 189), followed by identity (RPN = 100). Assay was also the most critical quality attribute (RPN = 378), followed by identity (RPN = 100) for Norfloxacin tablets. The risk-based desirability function approach showed that 75% (3/4) of ciprofloxacin products were of good quality, and 25% (1) were found to be of acceptable quality, while the desirability function of norfloxacin tablets was found to be excellent 1 (33.3%), good 1 (33.3%), and acceptable 1 (33.3%). The study revealed that medications can maintain their quality beyond their labeled expiration date. By combining pharmacopeial standards with risk-based approaches like failure mode and effect analysis (FMEA), the study provides a comprehensive evaluation framework. This approach not only confirms the continued effectiveness of expired fluoroquinolone antibiotics but also underscores the potential waste reduction and cost-saving benefits. This could significantly contribute to addressing healthcare challenges in low-resource settings, promoting more efficient pharmaceutical resource utilization.

Multi-response optimization in laser processing technologies by applying desirability function approach and response surface methodology based on grey relation analysis

Laser processing technologies are among the leading industry technologies for efficient and economical processing of a wide spectrum of engineering materials. The determination of optimal parameter settings with respect to multiple and opposite process performances is of great practical importance in laser processing technologies. This paper discusses, analyzes, and compares two main approaches for multi-response optimization (MRO) in engineering, that is, desirability function approach (DFA) and grey relation analysis (GRA), while solving five case studies, covering different laser processing technologies, such as cutting, drilling, welding, micro-channeling, and cladding. In each case study the MRO solutions obtained with two competitive approaches were assessed using the relative target deviation (RTD) criterion. Analysis of the obtained MRO solutions and comparative analysis with results from previous studies indicate that the integrated RSM-GRA provide similar and comparable solutions with the hybrid Taguchi method and GRA, while DFA proved to be a better and more promising approach for solving MRO in laser processing technologies. Some specific features, limitations, and possibilities of MRO approaches were also discussed.

Technology development of novel autogenous double pulse tungsten insert gas welding technique to evaluate the depth of penetration, micro segregation via machine learning and desirability function approach

The present research investigates the effect of process parameters on the microstructure, micro segregation, and material properties of the Autogenous Double Pulse Tungsten Inert Gas Welding (ADP-TIG) of Inconel 625. The optimization of the process parameter was evaluated by Response surface Methodology (RSM) Box Behnken method. In this article, the effects of the weld-bead geometry, weld centre (WC), weld interface (WI), and heat affected zone (HAZ) were evaluated through macro morphology, microstructure, SEM/EDAX, XRD, and micro hardness. The outcomes indicate that by employing a combination of high and low frequency pulsing in ADP-TIG, it is possible to regulate not only the dimensions of the weld-bead and penetration depth but also the HAZ. The ADP-TIG process has a low heat input, resulting in finer grains in the microstructure, reduced secondary phases, and a narrower HAZ. SEM images of alloy 625 confirms the depiction of fine equiaxed dendrites. The ADP-TIG process reduces secondary phase formation due to a reduction in Mo and Nb alloying elements. EDAX results also show that the micro segregation in the weld centre (WC) and weld interface (WI) is very less in the dendritic core region because of the lower heat Input. The XRD result confirms the presence of NbzNi, Cr₂Nb, Ni8Nb, Fe2Mo3 and FeNi phases. The maximum microhardness values in WC and WI are 290 HV and 280 HV, respectively. The impacts of ADP-TIG welding technique specifications on factors such as weld bead geometry, WC, WI, and HAZ have been thoroughly examined across a range of process parameters. Further the process parameters of ADP-TIG were optimized using a desirability function to achieve defect-free welds, followed by experimental validation. The desirability function results were then compared with a machine learning (ML) approach. The investigation aimed to assess ML algorithms' predictive capability for weldment outcomes. From the ML modelling results, Support Vector Machine (SVM) using the Radial Basis Function (RBF) kernel was identified as the superior strategy for accurately predicting Heat input, Mo segregation, and Nb segregation. Additionally, Gaussian process regression (GPR) with an RBF kernel was recommended for predicting the Width to Depth Ratio.

Valorisation of bottom ash in concrete: serviceability, microstructural and sustainability characterisation

The present study investigated the synergistic influence of bottom ash as a Portland cement (PC) and natural fine aggregate (NFA) replacement in concrete. Coal bottom ash (CBA) is a heavy ash that settles at the bottom of the combustion chamber of a thermal power plant. It was ground (GCBA) for two hours prior to replacing 10–30% PC, while CBA was used in raw form to replace 25% and 50% NFA. The mechanical properties along with durability properties (accelerated carbonation and chloride penetration) were studied after 28 days and 90 days of curing. Non-destructive tests were also performed to check the quality of CBA-based concrete. Microstructural characterisation was conducted using various techniques, namely X-ray diffraction, scanning electron microscopy and Fourier transform infrared spectroscopy. The concrete with 20% GCBA and 25% CBA (G20C25) reported the best performance in terms of parameters studied owing to the pozzolanic reactivity of GCBA and the filler effect of fine CBA. The microstructural investigations also validated the findings and trends observed in the experimental results. Well-fitted mathematical models were derived and optimisation was carried out using the desirability function approach. Multi-objective optimisation recommended 21.80% GCBA and 24.17% CBA as the optimum amount, resulting in a significant reduction of 19.19% and 18.19% in carbon footprint and eco-cost, respectively, compared to the control mix.

Study on adaptive regulation based on water supply-demand system structure and water use desirability under extreme drought

The frequent occurrence of drought events has changed the structure of the water supply-demand system, seriously hindering the sustainable development of society. This study aims to address the challenges of unclear impact of the system structure changes on water use desirability (WUD), as well as the regional water resources adaptive regulation. First, the structure of the water supply-demand system was analyzed based on the system theory. Then water supply priority (WSP) and water use compression (WUC) were considered to set combined regulation scenarios, which respectively characterize the structure of the water supply and demand systems. The MIKE BASIN/NAM water resources regulation coupling model was constructed to simulate the water use process under different scenarios. The desirability function approach was introduced to quantify the impact of system structure changes on WUD, and ultimately the adaptive regulation strategies were proposed with the goal of the highest WUD. Taking Chuxiong Prefecture in Yunnan, China as the study area, the validity of the model was verified, and the main results show that (1) t he optimal WSP in Chuxiong Prefecture was: urban domestic > rural domestic > industry > agriculture; (2) agriculture was identified as the most appropriate water user for compressing; (3) it was recommended that the adaptive regulation measure was to adjust the WSP in Dayao, Mouding and Wuding; and change the WUC in Yongren and Yao'an. In Nanhua, Chuxiong, Shuangbai and Lufeng, the adaptive regulation measure was to joint adjustment of WSP and WUC. These results clarify the impact of the system structure changes on WUD in Chuxiong Prefecture, identifying the optimal WSP and optimal WUC user under extreme drought, and propose water resources adaptive regulation suggestions for each county. In addition, the water resources adaptive regulation technology proposed in this study can provide scientific basis for water resources allocation under future climate change.

Drying techniques for precooked hybrid rice to ensure quality of instantized rice

AbstractBackground and ObjectivesDue to the emergence of new hybrid rice cultivars, rice processors must refine their processing protocols to maintain the production of high‐quality products. This is crucial for the instant rice processing industry. Therefore, this study aims to refine current convective heated air‐drying processing methods to suit the new hybrid rice cultivars intended for instant rice production. New strategies to dry cooked rice were investigated. These included drying in a stepwise manner with high (230–200°C) and medium (190–160°C) air temperatures and drying at constant high (200°C) and medium (160°C) air temperatures. Changes in quality attributes of two new long‐grain (LG) hybrid rice cultivars (XL 753 and RT 7521 FP), including rehydration ratios, texture, color, and bulk density, were examined after using the modified drying strategies.FindingsThe percentage points of moisture removed were significantly affected by cultivar type, cooked rice drying air temperatures, and relative humidity (RH) (p &lt; .05). Additionally, the rehydration ratio of instantized rice was significantly influenced by the drying air temperature (p = .0361). The highest rehydration ratio (3.14) was observed for cultivar XL 753, which was dried at a constant air temperature of 160°C and 40% RH, whereas the lowest (2.77) was observed with the same cultivar when dried at a constant air temperature of 200°C and 40% RH. The bulk density and volume increase ratio were impacted by cultivar type (p &lt; .05), while drying air temperature and RH did not have significant effects on the volume increase ratio. Water activity values ranged from 0.53 to 0.56 for both cultivars and were influenced by drying air temperature, while color was affected by RH (p &lt; .05). Textural parameters (hardness, adhesiveness, springiness, and cohesiveness), except resilience and springiness, were significantly affected by cultivar type, while drying air temperature had significant impacts on all textural parameters.ConclusionsPrediction profiler for multi‐attribute optimization using a desirability function approach from JMP suggested the XL753 cultivar, an RH of 20%, and Stepwise High as the drying treatment. Under these settings, the predicted percent point of moisture content loss, rehydration ratio, ΔE value (color), bulk density, hardness, adhesiveness, springiness, and cohesiveness of the models were 36.75, 9.44, 0.33, 2.95 g/mL, 3941.60 g, −14.17 gs, 0.72, and 0.59, respectively. The study provides crucial information for guiding decisions on drying conditions for industrial processing of instantized rice that may yield the most desirable quality attributes. Different rice cultivars may have differences in the quality of instant rice; however, the drying treatment found to work best in this study should be valid for all rice cultivars.Significance of StudyThe study has provided crucial information on the possibility of modifying convective heated air‐drying methods to improve the quality attributes of instant rice made from new hybrid rice cultivars. Information generated from this study may be useful to guide decisions on drying conditions for industrial processing of instantized rice with premium quality.

Optimizing hydrothermal synthesis of titanium dioxide nanotubes: Doehlert method and desirability function approach

Optimizing hydrothermal synthesis of titanium dioxide nanotubes: Doehlert method and desirability function approach

Modelling of Proximate Composition of Amaranth, Sorghum, Pumpkin and Sunflower Flour Blends using Response Surface Methodology

Blends of cereals and legumes have gained attention especially in complementary nutrition. Optimization of the production of composite flour from defatted amaranth, sorghum, defatted pumpkin and defatted sunflower flour using D-optimal mixture design of Design Expert software with the levels for amaranth (40-60 %), sorghum (10-30 %), pumpkin (20-40 %), and sunflower flour (3-10 %), respectively was carried out. The responses were proximate composition. Run 5 and 14 recorded the highest crude protein (4.08 %) and total ash content (5.71 %) while run 7 and 10 had the highest fibre content (4.08 %.), respectively. The model terms were significant (p≤0.05) for the proximate composition of the blends with R2 values of 0.93, 0.92, 0.96, 0.90, 0.91, and 0.90 respectively for moisture, protein, fat, ash, fibre, and carbohydrate. The optimal blend from the numerical optimization through the desirability function approach were 42.46% amaranth, 10.00% sorghum, 40.00% pumpkin, and 7.54%. In conclusion, composite flour from amaranth, sorghum, pumpkin and sunflower flour have acceptable proximate composition in terms of nutritional quality necessary for the production of nutrient-dense food products capable of addressing issues of malnutrition.

Shear strength characterization and sustainability assessment of coal bottom ash concrete

The present study investigated the synergistic influence of coal bottom ash (CBA) on the shear strength of concrete. CBA was milled for 2, 6, and 10 h to form grinded CBA (GCBA). “L” shaped specimens were prepared with 10%–30% GCBA and 25%–50% CBA as alternative of Portland cement and natural fine aggregates. Concrete containing 20% GCBA (grinded for 6 h) and 25% CBA reported the highest shear strength owing to pozzolanic reactiveness and filler action. X-ray diffraction, scanning electron microscopy–energy-dispersive spectroscopy and Fourier transform infrared also supported the experimental outcomes. Well fitted mathematical models were derived followed by optimization using desirability function approach recommending 5.71 h of grinding, 26.27% GCBA, and 36.69% CBA as the optimum amount for its successful utilization in concrete. This approach further leads to significant reduction of about 22% in carbon footprints and eco-costs in comparison to conventional concrete.

Experimental investigation to assess the surface integrity in WEDM of Al-based hybrid composite material

Aluminum-based hybrid composites are used in various engineering applications due to their unique properties, such as high strength, good wear resistance, excellent corrosion resistance, and low density. Aluminum metal matrix (AMMC) composites reinforced with ceramic particles can be more difficult to machine than traditional aluminum alloys because of their increased hardness and abrasiveness. Wire electric discharge machining is a non-traditional machining process that uses a thin metal wire, typically made of molybdenum, brass or tungsten to cut through a workpiece using electrical discharge sparks. This process is useful for precision machining of complex shapes and profiles in hard or difficult to machine materials. WEDM is a commonly used process for cutting Al-SiC-ZrO2 HAMMCs due to their hardness and difficulty in machining using conventional techniques. The present study examines the relationship between machining parameters like pulse on time (TON), pulse off time (TOFF), peak current (IP) and wire feed (WF) and material removal rate (MRR) and surface roughness ( Ra) for Al-SiC-ZrO2 in WEDM. The Box-Beckham Design was used to plan the experiments, and response surface method was employed to develop the models. The pulse duration, TOFF, IP, and quadratic terms of [Formula: see text] and [Formula: see text] are the most influential factors of the MRR. The TON, IP and interaction term (TON × IP), and [Formula: see text] were the most significant factors for Ra. Multi-response optimization of process parameters was obtained using the desirability function approach. Furthermore, the machined samples were analyzed for surface integrity using SEM techniques. The prediction from this model validated the confirmation results.

Recycled rubber wastes-based polymer composites with flame retardancy and electrical conductivity: Rational design, modeling and optimization

Polymer recycling techniques experience a maturity period of design and application. Rubbers comprise a high proportion of polymer wastes, highly flammable and impossible to re-melt. Polymer composites based on ground tire rubber (GTR) and ethylene-vinyl acetate copolymer (EVA) containing carbon black (CB) (1–50 phr), with variable EVA/GTR weight composition (10/90, 25/75, 50/50, 75/25 and 90/10), and processing temperature (Low: 100 °C and High: 200 °C) were designed applying Design of Experiments (DOE) approach of Optimal Design. The properties and performance features were experimentally evaluated. The tensile strength (TS) and elongation at break (EB) were optimized using Desirability Function (DF) approach. A wide fluidity window (Labeled POOR, GOOD, and EXCELLENT) and mechanical properties were observed. Overall, higher values of EB were assigned to samples processed at 200 °C. Cubic regression modeling and DF optimization of TS and EB indicated unlikely that one expect a TS ≥ 3 MPa, while EB values more than 500% were likely regardless of CB content for EVA-rich composites. The electrical properties of CB/EVA/GTR samples were examined by impedance spectroscopy technique. An interesting relationship was observed between the DC conductivity and the EVA/GTR ratio and processing temperature. GTR-rich samples showed much higher conductivity than EVA-dominated samples, attributed to the presence of CB in the rubber waste, which, together with the added CB, was able to create conduction paths for the transported electrons. Higher processing temperature of 200 °C improved dispersion of the added CB, i.e. a more even distribution of the conductive phase in the matrix. The uniform and evenly dispersed domains of particles were detected by SEM images for highly CB loaded composites. Surprisingly, 50/50 EVA/GTR composites were resilient against flame, while thermally stable ones in TGA measurements were highly CB loaded ones.

Bionanofactory for green synthesis of collagen nanoparticles, characterization, optimization, in-vitro and in-vivo anticancer activities

Collagen nanoparticles (collagen-NPs) are promising biological polymer nanoparticles due to their exceptional biodegradability and biocompatibility. Collagen-NPs were bio-fabricated from pure marine collagen using the cell-free supernatant of a newly isolated strain, Streptomyces sp. strain NEAA-3. Streptomyces sp. strain NEAA-3 was identified as Streptomyces plicatus strain NEAA-3 based on its cultural, morphological, physiological properties and 16S rRNA sequence analysis. The sequence data has been deposited under accession number OR501412.1 in the GenBank database. The face-centered central composite design (FCCD) was used to improve collagen-NPs biosynthesis. The maximum yield of collagen-NPs was 9.33 mg/mL with a collagen concentration of 10 mg/mL, an initial pH of 7, an incubation time of 72 h, and a temperature of 35 °C. Using the desirability function approach, the collagen-NPs biosynthesis obtained after FCCD optimization (9.53 mg/mL) was 3.92 times more than the collagen-NPs biosynthesis obtained before optimization process (2.43 mg/mL). The TEM analysis of collagen-NPs revealed hollow sphere nanoscale particles with an average diameter of 33.15 ± 10.02 nm. FTIR spectra confirmed the functional groups of the collagen, collagen-NPs and the cell-free supernatant that are essential for the efficient capping of collagen-NPs. The biosynthesized collagen-NPs exhibited antioxidant activity and anticancer activity against HeP-G2, MCF-7 and HCT116 cell lines. Collagen-NPs assessed as an effective drug loading carrier with methotrexate (MTX), a chemotherapeutic agent. The TEM analysis revealed that the average size of MTX-loaded collagen-NPs was 35.4 ± 8.9 nm. The percentages of drug loading (DL%) and encapsulation efficiency (EE%) were respectively 22.67 and 45.81%.

Optimization of CNC-FSSW parameters for dissimilar spot welding of AA6061 aluminium alloy and mild steel using Taguchi based desirability function approach

Optimization of CNC-FSSW parameters for dissimilar spot welding of AA6061 aluminium alloy and mild steel using Taguchi based desirability function approach

A Box-Behnken design-based chemometric approach to optimize the sono-photodegradation of hydroxychloroquine in water media using the Fe(0)/S2O82-/UV system.

The huge utilization of hydroxychloroquine in autoimmune infections led to an abnormal increment in its concentration in wastewater, which can pose a real risk to the environment, necessitating the development of a pretreatment technique. To do this, we are interested in researching how hydroxychloroquine degrades in contaminated water. The main goal of this investigation is to optimize the operating conditions for the sono-photodegradation of hydroxychloroquine in water using an ultrasound-assisted Fe(0)/ /UV system. To get adequate removal of HCQ, a chemometric method based on the Box-Behnken design was applied to optimize the influence of the empirical parameters selected, including Fe(0) dose, concentration, pH, and initial HCQ concentration. The quadratic regression model representing the HCQ removal rate (η(%)) was evolved and validated by ANOVA. The optimal conditions as a result of the above-mentioned trade-off between the four input variables, with η(%) as the dependent output variable, were captured using RSM methodology and the composite desirability function approach. For HCQ full decomposition, the optimal values of the operating factors are as follows: dose, 194.309 mg/L; Fe(0) quantity, 198.83 mg/L; pH = 2.017, and HCQ initial dose of 296.406 mg/L. Under these conditions, the HCQ removal rate, achieved after 60 min of reaction, attained 98.95%.